Ideation, Creativity and Concept Selection

Introduction and Context

Product development begins before a specification, prototype or business case exists. It begins with an attempt to identify a valuable problem and imagine a credible response. Ideation is the deliberate generation, development and communication of ideas that may become products, services, features, business models or process improvements. Creativity supplies the variety of possibilities; concept selection supplies the discipline needed to decide which possibilities deserve investment.

For product managers, this distinction is essential. A creative workshop can generate hundreds of suggestions, but an organisation cannot investigate every suggestion equally. It must choose concepts that are desirable to users, feasible to build and operate, viable as an economic proposition, aligned with strategy and acceptable in terms of risk. Concept selection is therefore not merely a voting exercise. It is a structured decision-making activity conducted under uncertainty.

This chapter presents practical approaches to design thinking, divergent and convergent thinking, co-creation, creativity techniques and concept screening. It also explains weighted decision matrices, assumptions, evidence, bias, ethical considerations and the role of digital and artificial-intelligence tools.

Learning Objectives

By the end of this chapter, students should be able to:

1. Distinguish creativity, ideation, invention, innovation and product concepts.
2. Plan and facilitate structured ideation and co-creation activities.
3. Apply design-thinking principles to product opportunity development.
4. Generate, cluster and refine ideas without prematurely judging them.
5. Evaluate concepts using desirability, feasibility, viability, strategic fit and risk.
6. Construct and interpret a weighted concept-selection matrix.
7. Recognise bias, ethical risks and limitations in concept selection.
8. Communicate a defensible recommendation to stakeholders.

1. From Opportunity to Product Concept

An idea is an initial possibility, often expressed in a sentence or sketch. A concept is a more developed proposition that explains the target user, the problem, the proposed experience or solution, and the expected value. A product concept should normally answer five questions:

• Who experiences the problem or opportunity?
• What outcome does that person or organisation seek?
• What is the proposed solution or experience?
• Why is the proposal better than current alternatives?
• What evidence and capabilities are required to develop it?

A concept is not the same as a feature list. “Add artificial intelligence” is an idea fragment; “help first-year students create a weekly study plan from their timetable, assessment deadlines and preferred study times” is closer to a product concept because it identifies a user, a job and an intended outcome.

Creativity, invention and innovation

Creativity is the ability to produce ideas that are both novel and appropriate to a purpose. Invention is the creation of a new device, method or arrangement. Innovation occurs when a new or improved idea is implemented and creates value. Thus, a creative idea has no organisational value unless it can be developed, adopted and sustained.

Innovation may be incremental, such as improving battery life, or more radical, such as introducing a new category of service. Radicality is relative to the organisation, market and technology: a feature may be ordinary in one industry but highly novel in another.

Product concepts as hypotheses

A concept should be treated as a set of hypotheses rather than as a proven solution. Typical hypotheses include:

• a particular user segment has an important unmet need;
• users will change their behaviour;
• the proposed solution will produce a meaningful benefit;
• the organisation can build, deliver and support it;
• customers or funders will provide sufficient economic value.

This perspective encourages teams to test assumptions before committing substantial resources. It also prevents attachment to an attractive idea from being mistaken for evidence of demand.

2. Design Thinking and the Logic of Ideation

Design thinking is a human-centred approach to innovation that integrates understanding of people, problem framing, idea generation, experimentation and learning. Common descriptions use the activities empathise, define, ideate, prototype and test. These activities are not a strictly linear sequence. Teams may return to research after testing a prototype or redefine the problem when evidence challenges the original framing.

Empathise and understand

Empathy in product development does not mean simply asking users what features they want. It involves understanding goals, constraints, emotions, workarounds, social context and the environment in which behaviour occurs. Useful methods include interviews, observation, diary studies, journey mapping, support-ticket analysis and contextual inquiry.

A strong research question explores behaviour and outcomes: “Tell me about the last time you had to do this” is generally more informative than “Would you use our proposed app?” The latter invites speculation and socially desirable answers.

Define the opportunity

Research findings should be synthesised into a problem or opportunity statement. A useful statement identifies a user, a need and a context without prescribing a solution. For example:

Commuting students who work part-time need a reliable way to coordinate changing study commitments because existing calendars do not combine institutional deadlines, travel time and personal availability.

This statement is more productive than “We need a better calendar,” because it leaves room for multiple solutions.

Ideate

Ideation is the divergent phase: the team seeks a range of possible responses. Quantity can be useful because early ideas stimulate combinations and variations. However, quantity alone is not creativity. The team must include varied perspectives, use constraints productively and later assess whether ideas address a meaningful opportunity.

Prototype and test

A prototype is a representation used to learn. It may be a paper sketch, storyboard, clickable interface, role-play, physical model, landing page or service blueprint. Prototypes should be inexpensive and proportionate to the uncertainty being investigated. If the uncertain issue is information architecture, a paper flow may be sufficient; if it is technical performance, a technical experiment may be required.

Testing should seek evidence about behaviour and outcomes, not merely approval. A user completing a task, selecting a workaround or agreeing to a realistic commitment is generally stronger evidence than a positive opinion.

3. Divergent and Convergent Thinking

Effective ideation alternates between expansion and focus.

Divergent thinking generates alternatives, perspectives and combinations. Judgement is deliberately delayed so that premature criticism does not eliminate promising directions. Convergent thinking evaluates, combines, narrows and prioritises alternatives using explicit criteria and evidence.

A common failure is premature convergence: a senior person proposes an idea, the group agrees immediately, and other possibilities disappear. The opposite failure is endless divergence: the team keeps generating ideas without making choices or learning from evidence. Product managers manage the transition between these modes by defining time limits, decision rules and evidence requirements.

Conditions for productive ideation

A well-designed session usually includes:

1. A clear opportunity statement and scope.
2. Relevant research evidence presented before generation.
3. A psychologically safe environment in which people can disagree with ideas without attacking people.
4. Individual silent generation before group discussion, reducing conformity and allowing quieter participants to contribute.
5. A visible record of ideas and assumptions.
6. A separate evaluation stage with agreed criteria.
7. A documented next step for the strongest concepts.

Brainstorming is often used as a generic label, but unstructured brainstorming can be dominated by fast speakers and familiar ideas. Facilitation is therefore a product-management capability rather than an administrative detail.

4. Creativity Techniques

Different techniques produce different kinds of output. The facilitator should select a method according to the problem, participants and desired level of novelty.

Brainwriting

Participants write ideas independently before sharing them. In a 6–3–5 format, six participants may write three ideas in five minutes and then build on ideas passed around the group. The precise numbers can be adapted; the underlying principle is structured participation rather than mandatory silence or rigid arithmetic.

“How might we?” questions

“How might we?” reframes a problem as an open design challenge. For example, “How might we help commuters use waiting time productively without requiring a laptop?” The wording should be broad enough to permit alternatives but specific enough to prevent irrelevant ideas.

SCAMPER

SCAMPER prompts teams to Substitute, Combine, Adapt, Modify, Put to another use, Eliminate and Reverse or rearrange aspects of an existing product or process. It is particularly useful for incremental innovation and service improvement.

Analogy and metaphor

Teams can examine how another domain solves a related problem. A hospital triage process, for example, may inspire prioritisation in a customer-support service. Analogies should stimulate hypotheses, not be treated as proof that a solution will transfer unchanged.

Constraint-based ideation

Constraints can increase originality. Prompts such as “design for a user with no smartphone,” “remove the need for training” or “deliver the benefit in two minutes” force teams to reconsider assumptions. Constraints must be realistic enough to be meaningful and not so narrow that they predetermine the answer.

Morphological analysis

A complex concept can be decomposed into dimensions, such as customer segment, channel, revenue model and service mechanism. Teams list alternatives for each dimension and combine them into configurations. This approach helps reveal possibilities that ordinary discussion misses.

5. Co-Creation and Stakeholder Participation

Co-creation involves users, customers, employees, partners or other stakeholders participating in the development of value propositions. It differs from consultation. In consultation, the organisation asks for opinions and retains most decision authority. In co-creation, participants contribute to framing, generating, interpreting or testing solutions.

Co-creation can improve relevance, uncover tacit knowledge and build commitment. A frontline employee may understand operational exceptions that a design team has never observed. A customer may identify a social or emotional requirement that survey data conceals.

However, co-creation is not automatically representative. Participants may be unusually enthusiastic, available or digitally confident. Product managers should document recruitment, incentives, participant characteristics and exclusions. Vulnerable participants require additional care, especially where health, finance, education or personal identity is involved.

Useful co-creation formats include:

• participatory workshops;
• customer journey and service-blueprint sessions;
• lead-user communities;
• prototype review and usability sessions;
• supplier and partner design reviews;
• online idea communities;
• internal hackathons connected to real customer evidence.

Participants should understand the purpose of the activity, how their contributions will be used and whether they will receive recognition or compensation. Organisations should not imply that every suggestion will be implemented.

6. From Ideas to Concepts

A large list of raw ideas should be transformed into a smaller set of coherent concepts. This involves clustering similar ideas, removing duplicates, combining complementary elements and identifying the assumption behind each proposal.

An affinity map groups observations or ideas by relationship. Clusters may reveal themes such as convenience, trust, personalisation or cost reduction. The team should avoid treating the number of sticky notes in a cluster as a direct measure of market importance; frequency can reflect who attended the workshop rather than customer value.

A concept brief commonly contains:

Concept articulation is a form of disciplined creativity. It makes alternatives comparable without pretending that uncertainty has disappeared.

7. Criteria for Concept Selection

Concept selection should reflect the organisation’s purpose and decision context. Five criteria are especially important.

Desirability

Desirability concerns whether a defined user or customer has a meaningful problem and values the proposed outcome. Evidence may include observed behaviour, retention in an existing workaround, pre-orders, credible commitments, task success or repeated use of a prototype. Stated interest is useful but weak when isolated from behaviour.

Feasibility

Feasibility concerns whether the organisation can build, operate, maintain and scale the concept. It includes technical architecture, data access, manufacturing, supply chain, service operations, skills, security and regulatory capability. A concept can be desirable but infeasible with current resources; this may justify a partnership or staged development rather than immediate rejection.

Viability

Viability concerns whether the concept can create sustainable economic or mission value. Relevant issues include price, revenue, cost to serve, acquisition cost, retention, capital requirements, cash timing and strategic benefits that are not captured by direct revenue. Public-sector and social products may use outcomes, funding continuity or cost avoidance rather than conventional profit as viability measures.

Strategic fit

Strategic fit asks whether the concept supports the organisation’s direction, capabilities, brand, portfolio and risk appetite. A highly attractive standalone product may still be a poor choice if it distracts from the organisation’s strategic position or creates destructive channel conflict.

Risk and learning value

Risk includes market, technical, operational, legal, security, reputational and ethical exposure. Risk should not be treated only as a reason to reject a concept. A low-cost experiment may reduce uncertainty substantially. The value of learning is therefore relevant: a concept may deserve an experiment because it tests a critical assumption, even if its current score is uncertain.

8. Weighted Decision Matrices

A weighted decision matrix converts qualitative judgements into a transparent comparison. It does not produce objective truth. Its value lies in making criteria, weights, evidence and disagreements visible.

Suppose each concept is scored from 1 to 5 on criteria, where 1 is very weak and 5 is very strong. Let s_ij be the score of concept i on criterion j, and let w_j be the weight of criterion j. If weights sum to 1, the weighted total for concept i is:

Here, T_i is the total score, m is the number of criteria, w_j is dimensionless and s_ij is the chosen rating. The formula assumes that the criteria are sufficiently defined for scores to be comparable, that higher scores consistently mean better performance, and that additive trade-offs are acceptable. If weights are expressed as percentages summing to 100, the total can be divided by 100 or reported on a 0–500 scale.

For criteria where a higher raw value is undesirable, such as implementation risk or cost, the scoring direction must be reversed or the criterion must be treated explicitly as a cost. Teams should never mix “high is good” and “high is bad” scores without documenting the transformation.

Worked example

A team compares three concepts for a study-planning service. It assigns the following weights: desirability 0.30, feasibility 0.25, viability 0.20, strategic fit 0.15 and risk-adjusted confidence 0.10. Scores range from 1 to 5.

For Concept A:

T_A = (0.30×5) + (0.25×3) + (0.20×4) + (0.15×5) + (0.10×2) = 1.50 + 0.75 + 0.80 + 0.75 + 0.20 = 4.00

For Concept B:

T_B = (0.30×4) + (0.25×5) + (0.20×3) + (0.15×4) + (0.10×4) = 1.20 + 1.25 + 0.60 + 0.60 + 0.40 = 4.05

For Concept C:

T_C = (0.30×3) + (0.25×4) + (0.20×5) + (0.15×3) + (0.10×3) = 0.90 + 1.00 + 1.00 + 0.45 + 0.30 = 3.65

Concept B has the highest weighted score, but the difference from Concept A is only 0.05 on a 1–5 scale. That small difference should not be presented as a decisive fact. The team should inspect the assumptions behind the scores and may run an experiment focused on Concept A’s feasibility and risk before making a final investment decision.

Improving the matrix

Good practice includes:

• define scoring anchors, such as what distinguishes a score of 2 from 4;
• separate facts from estimates and opinions;
• record evidence and confidence alongside each score;
• test whether changing plausible weights changes the ranking;
• use “must-have” gates for legal, safety or security requirements;
• discuss disagreements rather than averaging them away;
• combine the matrix with experiments, not use it as a substitute for evidence.

A weighted score can conceal a fatal weakness. For example, a concept with excellent desirability but unacceptable privacy risk should not pass merely because its average is high. Non-compensatory gates are therefore appropriate for minimum safety, compliance and ethical standards.

9. A Practical Concept-Selection Process

A disciplined process can be organised into seven stages.

Stage 1: Establish the decision. Define whether the team is selecting an idea for research, a prototype, a pilot or full development. Different stages require different evidence.

Stage 2: Define the opportunity and constraints. Clarify target users, strategic boundaries, budget, timing, regulatory conditions and non-negotiable requirements.

Stage 3: Generate broadly. Use more than one ideation technique and include relevant internal and external perspectives.

Stage 4: Develop concepts. Convert raw ideas into concept briefs with assumptions, intended outcomes and an initial evidence plan.

Stage 5: Apply an initial screen. Remove duplicates and concepts that clearly violate essential constraints. Avoid excessive detail at this stage.

Stage 6: Evaluate and test. Use a weighted matrix, qualitative discussion and targeted experiments. The experiment should address the most consequential uncertainty, not simply demonstrate that the team can build the concept.

Stage 7: Decide and document. Select, revise, defer or reject concepts. Record the rationale, evidence gaps, owners and conditions for revisiting the decision.

This process should be iterative. A concept may be reframed after research, merged with another concept or stopped after an invalidated assumption. Stopping a weak concept early is a productive outcome because it preserves resources.

10. Illustrative Case: CampusConnect Study Planning

The following is a fictional illustrative case.

Background

A college wants to improve academic support for diploma students who combine study with employment. A discovery team identifies three possible concepts:

• Concept A: Smart weekly planner—combines deadlines, timetable information and personal availability.
• Concept B: Peer study marketplace—matches students for paid or unpaid peer support.
• Concept C: Automated assessment coach—generates study prompts and feedback using institutional course material.

Problem

Interviews suggest that students struggle less with knowing that they should study than with coordinating irregular work shifts, travel and multiple deadlines. Staff are concerned about privacy, academic integrity and the accuracy of automated feedback.

Analysis

The team conducts observation and a low-fidelity prototype test. Students use the planner prototype to identify conflicts and reschedule tasks. Several participants request integration with existing calendars. The peer marketplace attracts interest but raises questions about safeguarding, payment and quality assurance. The automated coach produces promising prompts but occasionally misinterprets assessment requirements.

The team identifies key assumptions:

1. Students will provide or permit access to scheduling information.
2. Calendar integration can be implemented securely.
3. Students will return to the planner each week.
4. Staff will accept the service’s data-management approach.

Approach and solution

The team does not immediately build the complete product. It selects Concept A for a limited pilot, subject to a privacy review and a technical investigation of calendar integration. It also retains a small research track for Concept C because its educational value remains uncertain. Concept B is deferred until safeguarding and operating-model questions are better understood.

The pilot uses a simple interface and manual support rather than sophisticated automation. Its purpose is to test whether students create and follow realistic study plans, not to prove the final technology.

Outcome

The pilot shows strong initial task completion but weak weekly return rates. Interviews reveal that students value conflict detection but dislike entering repeated information. The team therefore revises the concept toward automatic import with user control, clear consent and a weekly confirmation step. The concept-selection decision has generated learning that changes the product definition.

Learning points

• A compelling feature is not necessarily the most important user outcome.
• Feasibility includes privacy, governance and operations, not only software construction.
• A low-fidelity pilot can test behaviour before expensive development.
• Concept selection should permit deferral and revision, not only “go” or “no-go.”
• Evidence about repeated behaviour is more valuable than enthusiasm during a workshop.

11. Bias, Ethics and Governance

Concept selection involves human judgement and can reproduce organisational bias. Common biases include:

• Authority bias: favouring an idea because a senior person proposed it.
• Confirmation bias: seeking evidence that supports a preferred concept.
• Availability bias: overvaluing memorable examples or recent events.
• Anchoring: allowing the first score or estimate to influence later judgement.
• Optimism bias: underestimating cost, time and adoption difficulty.
• Groupthink: suppressing disagreement in the interests of harmony.
• Sunk-cost bias: continuing with a weak concept because resources have already been spent.

Mitigations include independent scoring before discussion, explicit evidence logs, a designated dissenting role, diverse participation, pre-mortems and review of rejected as well as selected concepts.

Ethical review should consider who benefits, who may be excluded, whether participation is informed and voluntary, and whether the product could create harm or unfair treatment. Products involving personal data require privacy-by-design practices such as data minimisation, purpose limitation, access control, retention limits and transparent consent. Safety-critical or high-impact concepts require proportionate testing, documentation and human accountability.

Generative AI in ideation

Generative-AI tools can produce alternative prompts, personas, naming options, scenarios and initial variations quickly. They can help a team overcome a blank page, but they do not replace user research or managerial accountability. Outputs may contain stereotypes, fabricated claims, copyrighted material or confidential-data leakage. Teams should avoid entering sensitive customer information into unapproved systems, verify factual assertions and disclose material AI involvement when governance requires it.

AI-generated ideas also tend to reflect patterns in existing data, which can encourage incremental and culturally narrow solutions. Human participants remain responsible for problem framing, ethical judgement, evidence interpretation and final decisions.

12. Challenges and Limitations

Ideation and selection methods have several limitations. Workshops can favour articulate participants and may not represent the market. Scoring matrices create an impression of precision even when estimates are uncertain. Customer research can reveal stated preferences without predicting actual adoption. Strategic fit may be used to protect existing assumptions rather than explore new opportunities. Finally, innovation outcomes are affected by timing, competition, regulation and implementation quality, none of which can be fully predicted at the ideation stage.

These limitations do not make the methods useless. They imply that product managers should use multiple forms of evidence, make uncertainty explicit, revisit decisions and distinguish a decision to learn from a decision to scale. The objective is not perfect prediction; it is a better sequence of reversible, evidence-based commitments.

Best-Practice Checklist

Before approving a concept for further investment, ask:

• Is the target user and important problem clearly defined?
• What evidence demonstrates desirability?
• Which assumptions are most uncertain and consequential?
• Can the concept be delivered safely, legally and operationally?
• How might value be created and sustained?
• Does the concept support strategy without relying on vague claims?
• Have risks and non-negotiable gates been assessed?
• Were diverse participants and dissenting views included?
• Are the scores and weights transparent?
• What is the cheapest credible experiment that could change the decision?
• Who owns the next step, and when will the decision be revisited?

Chapter Summary

Ideation is the structured generation and development of possibilities, while concept selection is the disciplined comparison of alternatives under uncertainty. Design thinking connects user understanding, problem framing, ideation, prototyping and testing in an iterative process. Product teams should alternate divergent thinking, which expands possibilities, with convergent thinking, which focuses effort using explicit evidence and criteria.

Creativity techniques such as brainwriting, “How might we?” questions, SCAMPER, analogy and constraint-based ideation support different forms of innovation. Co-creation brings users and stakeholders into development but requires careful recruitment, transparency and inclusion. Concepts become decision-ready when they specify a target user, problem, proposed experience, value proposition, assumptions, enablers, risks and evidence plan.

The principal selection criteria are desirability, feasibility, viability, strategic fit and risk. A weighted decision matrix can make trade-offs visible, but its scores are judgements rather than facts. Weights, scoring anchors, evidence confidence and non-compensatory safety or compliance gates must be made explicit. Ethical governance, bias mitigation and responsible use of AI are integral to professional product management. The strongest process does not simply select the most attractive concept; it identifies the next evidence-generating action and preserves the ability to learn, revise or stop.

Key Terms

Creativity: The production of ideas that are novel and appropriate to a purpose.

Ideation: The deliberate generation, development and communication of possible solutions.

Innovation: The implementation of a new or improved idea that creates value.

Product concept: A developed proposition describing a user, problem, solution and expected value.

Design thinking: A human-centred, iterative approach to understanding problems and developing solutions.

Divergent thinking: Expanding the range of ideas, perspectives and alternatives.

Convergent thinking: Narrowing and prioritising alternatives using criteria and evidence.

Co-creation: Collaborative development involving users, customers or other stakeholders.

Prototype: A representation used to learn about a concept or assumption.

Desirability: The extent to which users value the proposed outcome.

Feasibility: The extent to which a product can be built, delivered and supported.

Viability: The extent to which a product can create sustainable economic or mission value.

Strategic fit: Alignment with organisational direction, capabilities and portfolio.

Weighted decision matrix: A structured comparison using criteria, weights and scores.

Assumption: A proposition believed to be true but requiring evidence.

Affinity mapping: Grouping related observations or ideas to identify themes.

Premature convergence: Narrowing choices before sufficient alternatives or evidence exist.

Psychological safety: A climate in which participants can contribute and disagree without interpersonal punishment.

Risk-adjusted decision: A decision that accounts for uncertainty and possible adverse outcomes.

Concept gate: A required condition that a concept must satisfy before proceeding.

Review Questions

Short Answer Questions

1. What is the difference between an idea and a product concept?
2. Why should divergent and convergent thinking be separated during ideation?
3. Define desirability, feasibility and viability.
4. What is the purpose of a prototype?
5. Identify two biases that can affect concept selection.

Long Answer Questions

1. Explain how design thinking supports the movement from user research to concept testing.
2. Compare three creativity techniques and explain when each may be useful.
3. Describe how co-creation can improve product development and discuss its limitations.
4. Explain how to construct and use a weighted decision matrix, including its assumptions.
5. Discuss why ethical, privacy and governance considerations should be included during early concept selection.

Analytical / Application Questions

1. A team has ten raw ideas for improving a public transport app. Design a process that converts these ideas into two testable product concepts.
2. Construct suitable selection criteria and weights for choosing between a low-cost incremental feature and a high-risk new platform. Explain how you would prevent a high average score from concealing a critical safety or compliance problem.
3. A concept receives enthusiastic workshop feedback but users do not return to the prototype after one week. Analyse what this evidence means and recommend the next product-management action.

Multiple Choice Questions with Answers

Question 1

Which statement best describes a product concept?

A. A random list of possible features

B. A developed proposition linking a target user, problem, solution and expected value

C. A completed product ready for launch

D. A financial forecast without a user problem

Correct Answer: B. A developed proposition linking a target user, problem, solution and expected value

Question 2

What is the main purpose of divergent thinking in ideation?

A. To eliminate all risky ideas immediately

B. To calculate the final business case

C. To expand the range of possible alternatives

D. To select the preferred supplier

Correct Answer: C. To expand the range of possible alternatives

Question 3

Which activity is most consistent with user-centred research?

A. Asking only whether users like a proposed feature

B. Observing how users currently complete the relevant task

C. Scoring concepts before defining the problem

D. Selecting the idea proposed by the most senior participant

Correct Answer: B. Observing how users currently complete the relevant task

Question 4

In a weighted decision matrix, what does the weight usually represent?

A. The relative importance of a criterion

B. The number of users in a market

C. The development time in hours

D. The legal status of a product

Correct Answer: A. The relative importance of a criterion

Question 5

Using the chapter’s matrix formula, a concept scoring 4 on two criteria with weights 0.6 and 0.4 has what weighted total?

A. 2.0

B. 3.2

C. 4.0

D. 4.8

Correct Answer: C. 4.0

Question 6

Which criterion asks whether an organisation can build, operate and support a concept?

A. Desirability

B. Feasibility

C. Strategic fit

D. Novelty

Correct Answer: B. Feasibility

Question 7

Why may a concept with a high average score still be rejected?

A. All weighted matrices are mathematically invalid

B. A non-negotiable safety, legal or ethical requirement may be violated

C. High scores always indicate low desirability

D. Concepts must never be compared

Correct Answer: B. A non-negotiable safety, legal or ethical requirement may be violated

Question 8

Which is an example of premature convergence?

A. Generating ideas individually before group discussion

B. Testing a low-fidelity prototype

C. Accepting a senior manager’s idea before considering alternatives

D. Documenting assumptions and evidence gaps

Correct Answer: C. Accepting a senior manager’s idea before considering alternatives

Question 9

What is a suitable purpose of an early prototype?

A. To guarantee commercial success

B. To learn about an important assumption at low cost

C. To replace all customer research

D. To finalise every technical specification

Correct Answer: B. To learn about an important assumption at low cost

Question 10

Which practice supports responsible use of generative AI in ideation?

A. Treating AI output as verified market evidence

B. Uploading confidential customer data to any public tool

C. Using AI for alternatives while verifying claims and retaining human accountability

D. Allowing AI to make the final ethical decision

Correct Answer: C. Using AI for alternatives while verifying claims and retaining human accountability